9 results on '"Kuznetsova, Rita"'
Search Results
2. On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
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Kuznetsova, Rita, Pace, Alizée, Burger, Manuel, Yèche, Hugo, Rätsch, Gunnar, Kuznetsova, Rita, Pace, Alizée, Burger, Manuel, Yèche, Hugo, and Rätsch, Gunnar
- Abstract
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods' impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module., Comment: Machine Learning for Health (ML4H) 2023 in Proceedings of Machine Learning Research 225
- Published
- 2023
3. Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
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Jain, Samyak, Burger, Manuel, Rätsch, Gunnar, Kuznetsova, Rita, Jain, Samyak, Burger, Manuel, Rätsch, Gunnar, and Kuznetsova, Rita
- Abstract
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning advances have utilized patient time series data, and fusion models have incorporated unstructured clinical reports, improving predictive performance. However, integrating established medical knowledge into these models has not yet been explored. The medical domain's data, rich in structural relationships, can be harnessed through knowledge graphs derived from clinical ontologies like the Unified Medical Language System (UMLS) for better predictions. Our proposed methodology integrates this knowledge with ICU data, improving clinical decision modeling. It combines graph representations with vital signs and clinical reports, enhancing performance, especially when data is missing. Additionally, our model includes an interpretability component to understand how knowledge graph nodes affect predictions., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 11 pages
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- 2023
4. Language Model Training Paradigms for Clinical Feature Embeddings
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Hu, Yurong, Burger, Manuel, Rätsch, Gunnar, Kuznetsova, Rita, Hu, Yurong, Burger, Manuel, Rätsch, Gunnar, and Kuznetsova, Rita
- Abstract
In research areas with scarce data, representation learning plays a significant role. This work aims to enhance representation learning for clinical time series by deriving universal embeddings for clinical features, such as heart rate and blood pressure. We use self-supervised training paradigms for language models to learn high-quality clinical feature embeddings, achieving a finer granularity than existing time-step and patient-level representation learning. We visualize the learnt embeddings via unsupervised dimension reduction techniques and observe a high degree of consistency with prior clinical knowledge. We also evaluate the model performance on the MIMIC-III benchmark and demonstrate the effectiveness of using clinical feature embeddings. We publish our code online for replication., Comment: Poster at "NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice"
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- 2023
5. Multi-modal Graph Learning over UMLS Knowledge Graphs
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Burger, Manuel, Rätsch, Gunnar, Kuznetsova, Rita, Burger, Manuel, Rätsch, Gunnar, and Kuznetsova, Rita
- Abstract
Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical language system. These representations are aggregated to represent entire patient visits and then fed into a sequence model to perform predictions at the granularity of multiple hospital visits of a patient. We improve performance by incorporating prior medical knowledge and considering multiple modalities. We compare our method to existing architectures proposed to learn representations at different granularities on the MIMIC-III dataset and show that our approach outperforms these methods. The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge., Comment: Machine Learning for Health (ML4H) 2023 in Proceedings of Machine Learning Research 225
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- 2023
6. On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
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Husmann, Severin, Yèche, Hugo, Rätsch, Gunnar, Kuznetsova, Rita, Husmann, Severin, Yèche, Hugo, Rätsch, Gunnar, and Kuznetsova, Rita
- Abstract
Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that jointly using clinical notes with electronic health record (EHR) data improved predictive performance for patient monitoring in the intensive care unit (ICU). In this work, we explore the underlying reasons for these improvements. While relying on a basic attention-based model to allow for interpretability, we first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes. We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes. We believe such findings highlight deep learning models for EHR data to be more limited by partially-descriptive data than by modeling choice, motivating a more data-centric approach in the field., Comment: Workshop on Learning from Time Series for Health, 36th Conference on Neural Information Processing Systems (NeurIPS 2022) 15 pages (including appendices)
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- 2022
7. Temporal Label Smoothing for Early Event Prediction
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Yèche, Hugo, Pace, Alizée, Rätsch, Gunnar, Kuznetsova, Rita, Yèche, Hugo, Pace, Alizée, Rätsch, Gunnar, and Kuznetsova, Rita
- Abstract
Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging task is typically treated as a simple binary classification, ignoring temporal dependencies between samples, whereas we propose to exploit this structure. We first introduce a common theoretical framework unifying dynamic survival analysis and early event prediction. Following an analysis of objectives from both fields, we propose Temporal Label Smoothing (TLS), a simpler, yet best-performing method that preserves prediction monotonicity over time. By focusing the objective on areas with a stronger predictive signal, TLS improves performance over all baselines on two large-scale benchmark tasks. Gains are particularly notable along clinically relevant measures, such as event recall at low false-alarm rates. TLS reduces the number of missed events by up to a factor of two over previously used approaches in early event prediction.
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- 2022
8. HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
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Yèche, Hugo, Kuznetsova, Rita, Zimmermann, Marc, Hüser, Matthias, Lyu, Xinrui, Faltys, Martin, Rätsch, Gunnar, Yèche, Hugo, Kuznetsova, Rita, Zimmermann, Marc, Hüser, Matthias, Lyu, Xinrui, Faltys, Martin, and Rätsch, Gunnar
- Abstract
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods. While raw datasets, such as MIMIC-IV or eICU, can be freely accessed on Physionet, the choice of tasks and pre-processing is often chosen ad-hoc for each publication, limiting comparability across publications. In this work, we aim to improve this situation by providing a benchmark covering a large spectrum of ICU-related tasks. Using the HiRID dataset, we define multiple clinically relevant tasks in collaboration with clinicians. In addition, we provide a reproducible end-to-end pipeline to construct both data and labels. Finally, we provide an in-depth analysis of current state-of-the-art sequence modeling methods, highlighting some limitations of deep learning approaches for this type of data. With this benchmark, we hope to give the research community the possibility of a fair comparison of their work., Comment: NeurIPS 2021 (Datasets and Benchmarks)
- Published
- 2021
9. Variational learning across domains with triplet information
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Kuznetsova, Rita, Bakhteev, Oleg, Ogaltsov, Alexandr, Kuznetsova, Rita, Bakhteev, Oleg, and Ogaltsov, Alexandr
- Abstract
The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relative constraints or triplets that sampled from the shared latent space across domains. In other words, we combine the deep generative models with a metric learning ideas in order to improve the final objective with the triplets information. The performance of the VBTA model is demonstrated on different tasks: image-to-image translation, bi-directional image generation and cross-lingual document classification.
- Published
- 2018
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